The separation performance of the fastICA algorithm in auditory scene analysis

Abstract

The human sense of hearing is able to separate individual sounds in the environment with extraordinary accuracy. Several attempts have been made to imitate it artificially but so far none of these methods have achieved the performance of the human sense of hearing. The most useful of these methods is blind signal separation (BSS) since it does not need any preliminary information on the sound sources. This work used independent component analysis (ICA) for separating sound sources. ICA is an efficient blind signal separation technique. At the moment, ICA is able to separate sound sources well but it has limitations such as the amount of microphones used, statistical independence between sound sources, immobility of sound sources and the permutation and scaling problem.

The software developed for this Bachelor’s thesis is based on the FastICA algorithm, which is currently one of the fastest ICA algorithms in existence. The accuracy of FastICA is measured by calculating the coherence in the separation of 2 to 19 sound sources. The results indicate that the separation performance of FastICA decreases as the number of sources increases and similar sound sources are not separated all that well. Additionally, low frequency signals are separated poorly. The separation performance is excellent with less than eight sound sources and over 95 % of sounds will separate well. In conclusion, it can be said that further research is needed in order to develop methods that deal with the limitations of ICA.